Optimization of human mesenchymal stem cell manufacturing: the effects of animal/xeno-free media.

Due to their immunosuppressive properties, mesenchymal stem cells (MSC) have been evaluated for the treatment of immunological diseases. However, the animal-derived growth supplements utilized for MSC manufacturing may lead to clinical complications. Characterization of alternative media formulations is imperative for MSC therapeutic application. Human BMMSC and AdMSC were expanded in media supplemented with either human platelet lysates (HPL), serum-free media/xeno-free FDA-approved culture medium (SFM/XF), or fetal bovine serum (FBS) and the effects on their properties were investigated. The immunophenotype of resting and IFN-γ primed BMMSC and AdMSC remained unaltered in all media. Both HPL and SFM/XF increased the proliferation of BMMSC and AdMSC. Expansion of BMMSC and AdMSC in HPL increased their differentiation, compared to SFM/XF and FBS. Resting BMMSC and AdMSC, expanded in FBS or SFM/XF, demonstrated potent immunosuppressive properties in both non-primed and IFN-γ primed conditions, whereas HPL-expanded MSC exhibited diminished immunosuppressive properties. Finally, IFN-γ primed BMMSC and AdMSC expanded in SFM/XF and HPL expressed attenuated levels of IDO-1 compared to FBS. Herein, we provide strong evidence supporting the use of the FDA-approved SFM/XF medium, in contrast to the HPL medium, for the expansion of MSC towards therapeutic applications

Informatics-Based Discovery of Disease-Associated Immune Profiles

<div><p>Advances in flow and mass cytometry are enabling ultra-high resolution immune profiling in mice and humans on an unprecedented scale. However, the resulting high-content datasets challenge traditional views of cytometry data, which are both limited in scope and biased by pre-existing hypotheses. Computational solutions are now emerging (e.g., Citrus, AutoGate, SPADE) that automate cell gating or enable visualization of relative subset abundance within healthy versus diseased mice or humans. Yet these tools require significant computational fluency and fail to show quantitative relationships between discrete immune phenotypes and continuous disease variables. Here we describe a simple informatics platform that uses hierarchical clustering and nearest neighbor algorithms to associate manually gated immune phenotypes with clinical or pre-clinical disease endpoints of interest in a rapid and unbiased manner. Using this approach, we identify discrete immune profiles that correspond with either weight loss or histologic colitis in a T cell transfer model of inflammatory bowel disease (IBD), and show distinct nodes of immune dysregulation in the IBDs, Crohn’s disease and ulcerative colitis. This streamlined informatics approach for cytometry data analysis leverages publicly available software, can be applied to manually or computationally gated cytometry data, is suitable for any clinical or pre-clinical setting, and embraces ultra-high content flow and mass cytometry as a discovery engine.</p></div

Informatics-based identification of immune dysregulation in clinical inflammatory bowel diseases.

<p>(A) <i>Bottom left</i>, 6-parameter FACS panel used for analyzing expression of surface antigens on peripheral blood mononuclear cells (PBMC) from healthy adult donors and adult IBD patients. Gating strategy for FACS analysis of human PBMC; immune subsets used in downstream analysis are indicated by gates and text. (B) Percentages of major T cell subsets in a healthy control PBMC stock, determined by repeated FACS analysis as in (A), over 10 independent staining experiments. Each subset is quantified based on the percentages within relevant parent gates (as in (A)); coefficients of variation (CVs) are indicated for each subset by color-matched text. (C) Heat map showing hierarchical clustering of 7 disease endpoints and 24 immune phenotypes in healthy adults (<i>n</i> = 26) and IBD patients ((ulcerative colitis (UC), <i>n</i> = 50; Crohn’s disease (CD), <i>n</i> = 53). (D) Rank-ordered (Pearson <i>r</i>) correlation values of all disease endpoints and immune phenotypes relative to diagnosis group (i.e., healthy donors, group 1; UC patients, group 2; CD patients, group 3). Relevant disease endpoints and immune phenotypes are indicated by black and red text, respectively; the correlation of the reference variable with itself (<i>r</i> = 1.0) is shown at top left in grey. (E) Immune cell subsets (CD4⁺CD25^hi–<i>left</i>; CD8⁺RO^- Teff–<i>middle</i>; CD8⁺ naïve–<i>right</i>) identified by hierarchical clustering and ranked Pearson coefficients (as in (C, D)) perturbed in CD patient PBMC. (F) Immune cell subsets (CD4⁺ naive–<i>left</i>; CD4⁺ Teff–<i>right</i>) identified by hierarchical clustering and ranked Pearson coefficients (as in C and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163305#pone.0163305.s003" target="_blank">S3 File</a>) perturbed in UC PBMC. Red lines indicate median values for each group. * P < .05, ** P < .01, *** P < .001, One-way ANOVA. Teff, effector/memory T cells. Only significant differences between groups are shown.</p

Conversion of IBD patient clinical and demographic data to single numeric values for hierarchical clustering.

<p>Conversion of IBD patient clinical and demographic data to single numeric values for hierarchical clustering.</p

An informatics approach to correlating immune phenotpyes with weight loss or colitis in a T cell transfer mouse model of IBD.

<p>(A) Weight loss in FVB.<i>Rag1</i>^<i>-/-</i> mice (<i>n</i> = 9) injected with wild type naïve CD4⁺ T cells. Weights are shown relative to day 0 (pre-transfer baseline). Bold red trace shows mean weight loss for the group; green and blue traces show individual mice displaying mild or aggressive weight loss, respectively. Examples of disease severity index (DSI) calculations are shown in color-coded text. (B) Quantitative colitis scores (<i>n</i> = 9) from the same group of T cell-transferred FVB.<i>Rag1</i>^<i>-/-</i> mice shown in (A). H&E-stained colon tissues were scored blindly as in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163305#pone.0163305.ref017" target="_blank">17</a>]; representative micrographs (at right) show mild (score of 1) and severe (score of 3) inflammation (20x magnification). Red horizontal bar indicates mean colitis scores for the group. (C) <i>Left</i>, 10-parameter FACS panel used for analyzing <i>ex vivo</i> expression of surface antigens on leukocytes isolated from spleen, mesenteric lymph nodes (MLN), and colon lamina propria (colon) of FVB.<i>Rag1</i>^<i>-/-</i> mice injected as in (A). <i>Right</i>, Gating strategy for surface FACS analysis; immune subsets used in downstream analysis are indicated by gates, text, and where appropriate, percentages. (D) <i>Left</i>, 11-parameter FACS panel used for analyzing <i>ex vivo</i> expression of intracellular transcription factors and cytokines in leukocytes isolated from T cell-transferred FVB.<i>Rag1</i>^<i>-/-</i> mice as above. <i>Right</i>, Gating strategy for intracellular FACS analysis; immune subsets used in downstream analysis are indicated by gates, text, and where appropriate, percentages. (E) Heat map showing hierarchical clustering of 7 disease endpoints and 57 immune phenotypes in T cell-transferred FVB.<i>Rag1</i>^<i>-/-</i> mice as above. Dendrograms (far left) show the clustering relationship between the mice based on all disease endpoints and immunophenotypes.</p

Demographic data of healthy volunteers and IBD patients analyzed in the study.

<p>Demographic data of healthy volunteers and IBD patients analyzed in the study.</p

Discrete immune phenotypes correspond with T cell transfer-induced weight loss or colitis in <i>Rag1</i>^-/- mice.

<p>(A) Rank-ordered (Pearson <i>r</i>) correlation values of all disease endpoints and immune phenotypes relative to weight loss (disease severity index (DSI)), in FVB.<i>Rag1</i>^-/- mice injected with wild type naïve CD4⁺ T cells as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163305#pone.0163305.g001" target="_blank">Fig 1A</a>. Relevant disease endpoints and immune phenotypes are indicated by black and red text, respectively. Correlation between weight loss and colitis scores is further shown in insert, where blue text indicates the Pearson <i>r</i> correlation value. (B) Rank-ordered (Pearson <i>r</i>) correlation values of all disease endpoints and immune phenotypes relative to colitis scores, determined by histology, in the same T cell-transferred FVB.<i>Rag1</i>^-/- mice. Relevant immune phenotypes are indicated by red text; correlation with weight loss (DSI) is indicated by black text. For (A, B), the correlation of the reference variable with itself (<i>r</i> = 1.0) is shown at top left in grey. (C) Exemplar immune phenotypes that correlate with T cell transfer-induced weight loss (disease severity index (DSI)), (<i>left</i>), but not histologic colitis (<i>right</i>) in T cell-transferred FVB.<i>Rag1</i>^-/- mice. (D) Exemplar immune phenotypes that correlate with T cell transfer-induced colitis (<i>right</i>), but not weight loss (disease severity index (DSI)) (<i>left</i>). Pearson <i>r</i> correlation values are show in red (for correlations achieving statistical significance) and blue (for correlations not statistically significant). * P < .05, ** P < .01, *** P < .001, Pearson correlation test.</p

Schedule of visits and corresponding blood draws.

1<p>All sites except Uganda.</p>2<p>Uganda only weeks 66 through to 104.</p